doc-start-{mean/variance/min/max}  We obtain Wikidata QIDs for each DBpedia entity and map those to pretrained embeddings from PyTorch-BigGraph [1]}.
For each mention in a mention pair, we look up the vectors of (1) the linked action mention (where available), (2) of all event components, (3) of all linked entities in the surrounding sentence, (4) of all linked entities in a 5-sentence window around the mention and (5) of all linked entities in the first three document sentences. Between each of these groups, we compute the pairwise cosine similarity between all vectors and retain mean, variance, minimum and maximum similarity respectively.
